Broadcast-enhanced key predistribution schemes

We present a formalisation of a category of schemes that we refer to as broadcast-enhanced key predistribution schemes (BEKPSs). These schemes are suitable for networks with access to a trusted base station and an authenticated broadcast channel. We demonstrate that the access to these extra resources allows for the creation of BEKPSs with advantages over key predistribution schemes such as flexibility and more efficient revocation. There are many possible ways to implement BEKPSs, and we propose a framework for describing and analysing them. In their paper “From Key Predistribution to Key Redistribution,” Cichoń et al. [2010] propose a scheme for “redistributing” keys to a wireless sensor network using a broadcast channel after an initial key predistribution. We classify this as a BEKPS and analyse it in that context. We provide simpler proofs of some results from their paper, give a precise analysis of the resilience of their scheme, and discuss possible modifications. We then study two scenarios where BEKPSs may be particularly desirable and propose a suitable family of BEKPSs for each case. We demonstrate that they are practical and efficient to implement, and our analysis shows their effectiveness in achieving suitable trade-offs between the conflicting priorities in resource-constrained networks

A Cross-Layer Design Framework for Wireless Sensor Networks with Environmental Monitoring Applications

In the past few years, wireless sensor networks (WSNs) are becoming more and more attractive because they can provide services that are not possible or not feasible before. In this paper, we address the design issues of an important type of WSNs, i.e., WSNs that enable environmental monitoring applications. We first provide an overview and analysis for our ongoing research project about a WSN for coastal-area acoustic monitoring. Based on the analysis, we then propose a cross-layer design framework for future WSNs that provide environmentalmonitoring services. The focus of the framework is the network layer design and the key idea of the framework is to fully understand and exploit both the physical layer characteristics and the requirements of upper layer applications and services. Particularly, for the physical layer characteristics, our framework 1) can enable advanced communication technologies such as cooperative communication and network coding; 2) can utilize the transmission characteristics for identifying/authenticating asender; and 3) can exploit the communication pattern as a mean of sensing. For the requirements of applications and services, our framework 1) is service-oriented; 2) can enable distributed applications; 3) can utilize the fact that many applications do not have strict delay constraints. To illustrate the advantages of the framework, we also conduct a case study that may be a typical scenario in the near future. We believe that our study in this work can provide a guideline for future WSN design

A Cross-Layer Design Framework for Wireless Sensor Networks with Environmental Monitoring Applications

In the past few years, wireless sensor networks (WSNs) are becoming more and more attractive because they can provide services that are not possible or not feasible before. In this paper, we address the design issues of an important type of WSNs, i.e., WSNs that enable environmental monitoring applications. We first provide an overview and analysis for our ongoing research project about a WSN for coastal-area acoustic monitoring. Based on the analysis, we then propose a cross-layer design framework for future WSNs that provide environmental monitoring services. The focus of the framework is the network layer design and the key idea of the framework is to fully understand and exploit both the physical layer characteristics and the requirements of upper layer applications and services. Particularly, for the physical layer characteristics, our framework 1) can enable advanced communication technologies such as cooperative communication and network coding; 2) can utilize the transmission characteristics for identifying/authenticating a sender; and 3) can exploit the communication pattern as a mean of sensing. For the requirements of applications and services, our framework 1) is service-oriented; 2) can enable distributed applications; 3) can utilize the fact that many applications do not have strict delay constraints. To illustrate the advantages of the framework, we also conduct a case study that may be a typical scenario in the near future. We believe that our study in this work can provide a guideline for future WSN design

A Learning-based Discretionary Lane-Change Decision-Making Model with Driving Style Awareness

Discretionary lane change (DLC) is a basic but complex maneuver in driving, which aims at reaching a faster speed or better driving conditions, e.g., further line of sight or better ride quality. Although many DLC decision-making models have been studied in traffic engineering and autonomous driving, the impact of human factors, which is an integral part of current and future traffic flow, is largely ignored in the existing literature. In autonomous driving, the ignorance of human factors of surrounding vehicles will lead to poor interaction between the ego vehicle and the surrounding vehicles, thus, a high risk of accidents. The human factors are also a crucial part to simulate a human-like traffic flow in the traffic engineering area. In this paper, we integrate the human factors that are represented by driving styles to design a new DLC decision-making model. Specifically, our proposed model takes not only the contextual traffic information but also the driving styles of surrounding vehicles into consideration and makes lane-change/keep decisions. Moreover, the model can imitate human drivers' decision-making maneuvers to the greatest extent by learning the driving style of the ego vehicle. Our evaluation results show that the proposed model almost follows the human decision-making maneuvers, which can achieve 98.66% prediction accuracy with respect to human drivers' decisions against the ground truth. Besides, the lane-change impact analysis results demonstrate that our model even performs better than human drivers in terms of improving the safety and speed of traffic